1 citations · 2 across the 13 of their papers we have counts for
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TAU-Bench: From Anomaly Instance Tracking to Fine-Grained Video Anomaly Understanding
Kepeng Yang, Dongxuan Liu, Rongxin Gao +8
Humans understand anomalous events through a coherent perceptual process in which they identify the focal instance, follow its behavior as the event unfolds, and interpret why it v…
Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation
Xinyu Liu, Zhen Chen, Wuyang Li +2
Transformers have shown remarkable performance in 3D medical image segmentation, but their high computational requirements and need for large amounts of labeled data limit their ap…
MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
Shengyuan Liu, Liuxin Bao, Qi Yang +6
Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous…
MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy
Wuyang Li, Wentao Pan, Xiaoyuan Liu +6
Miniaturized endoscopy has advanced accurate visual perception within the human body. Prevailing research remains limited to conventional cameras employing convex lenses, where the…
WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration
Chaojun Ni, Jie Li, Haoyun Li +8
Interactive 3D scene generation from a single image has gained significant attention due to its potential to create immersive virtual worlds. However, a key challenge in current 3D…
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline
Yuzhi Huang, Chenxin Li, Haitao Zhang +9
Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Although existing m…